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The purpose of this paper is to represent a pilot study which aims to explore the attention to sponsorship variable by assessing the level of congruence linked to both the sponsor and the sponsored entity (sponsee).

This research performed an experiment involving three different sporting activities where the level of congruence was perceived in a different way according to the different attributes of sponsorship message. Electroencephalograms were employed in order to measure reaction times when recognizing and identifying the level of congruence of sponsorship messages. The rate of successful understanding and identification of these sponsorship messages was also measured with this tool.

Incongruent, misfit messages are processed and encoded with a higher level of attention as opposed to the information which is reliable and consistent with prior expectations (congruent pairings). This means that subjects find fit, congruent messages more difficult to identify; in this case the information is poorly encoded and often misunderstood. In relation to attention congruity, this research found no differences across the different sporting activities which were examined.

This research proves that neuroscientific methods can be useful to assess the performance of sponsorship as an alternative to traditional techniques. In addition, this research contributes to the existing debate in the literature regarding the disagreeing results on the actual effectiveness of sponsoring congruent perceived events involving congruent perceived sports teams.

This paper is pioneering in the measurement of sponsorship performance through the use of electroencephalograms. Also, the level of attention is considered as a performance indicator.

To clarify the physiological and psychological effects of deep breathing, the effects of extreme prolongation of expiration breathing (Okinaga) were investigated using electroencephalogram (EEG) and electrocardiogram (ECG). Participants were five male Okinaga practitioners in their 50s and 60s. Participants performed Okinaga for 31 minutes while continuous EEG and ECG measurements were taken. After 16 minutes of Okinaga, and until the end of the session, the percentages of theta and alpha 2 waves were significantly higher than at baseline. After 20 minutes, and until the end of the session, the percentage of beta waves was significantly lower than at baseline. The high frequency component of heart rate variability was significantly lower after 12 minutes of Okinaga and lasted until 23 minutes. The low frequency/high frequency ratio was significantly lower after 18 minutes of Okinaga and until the end of the session. Okinaga produced relaxation, suggesting that deep breathing may relieve anxiety. However, study limitations include potential ambiguity in the interpretation of the low frequency/high frequency ratio, the small sample, and the fact that EEG was measured only on the forehead.

This paper aims to explain, first of all, signal modeling steps using Poincaré, and then considering the occurred events, concept of information applying Poincaré section and information approach, the brain pattern variations in autism spectrum disorder (ASD) cases will be diagnosed. A kind of representation of electroencephalogram (EEG) signal, namely, complementary plot, in which the main characteristic is special attention to asymmetry and symmetry coexist in natural and human processes, is introduced. In this paper, a new model is provided whose variations of patterns are similar to EEG’s when the transformation parameter is changed. A significant difference between ASD and healthy cases was also observed, which could be used to distinguish between various types of systems.

Complementary plot method is one of the most proper representations for Poincaré section of complex dynamics, because, as it was said about its characteristics, it has a qualitative approach toward signal (Sabelli, 2000, 2001, 2003, 2008, 2005, Sabelli et al., 2011). Considering the special conditions of this representation, here, intersection with a circle y² + x² = r² will be used; the important fact is, on the contrary to previous representations in which circular section had energy concept, here circular section considers phases. For finding trajectory intersection points, after calculating the sin and cosine of each term of EEG, plotting them in XY plane and drawing a chord between successive points of presentation transitions, then its intersections with the assumed circle are determined. But considering the sampling frequency, chords and Poincaré section, in this space, a minimum error – as the threshold – should be assumed in the program.

Natural and human processes are biotic (life-like) and creative (Sabelli and Galilei), and studying coexisting opposites by calculating the sine and cosine of each term in heartbeat intervals, weather variables and integer biotic series or random walk reveals an astonishingly regular mandala pattern; these patterns are not generated by random, periodic or chaotic series (Sabelli, 2005). This paper shows that in EEG of ASD children, mandala-like patterns of concentric rings are emergent in all situations (baseline – watching animation with voice and without voice) and electrode site (C3 and C4), but not in healthy individuals. The authors take the relation between sine and cosine functions as a mathematical model for complementary opposition, because it involves reciprocity and orthogonality sine and cosine are natural models for information. In fact, trigonometric analyses of empirical data to be described in this paper suggest expanding the concept of co-creative opposition to include uncorrelated opposites and partial opposites, i.e. partial agonists and partial antagonists that are neither linear nor orthogonal. Using Poincaré sections, it is shown that the difference in information and creativity of the data is the distinctive characteristic in ASD and healthy cases. Creation is the generation of novelty, diversity and complexity in complex systems.

This paper is an original paper based on cybernetic approaches for studying the variations of ASD children.

This work proposes classification of two-class motor imagery electroencephalogram signals using different automated machine learning algorithms. Here data are decomposed into various frequency bands identified by wavelet transform and will span the range of 0–30 Hz.

Statistical measures will be applied to these frequency bands to identify features that will subsequently be used to train the classifiers. Further, the assessment parameters such as SNR, mean, SD and entropy are calculated to analyze the performance of the proposed work.

The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.

The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.

This paper aims to review past papers focused on understanding consumer-related topics in marketing and related interdisciplinary fields to demonstrate the applications of electroencephalogram (EEG) in consumer neuroscience.

In addition to the review of papers using EEG to study consumer cognitive processes, the authors also discuss relevant decisions and considerations in conducting event-related potential (ERP) studies. Further, a framework proposed by Plassmann et al. (2015) was used to discuss the applications of EEG in marketing research from papers reviewed.

This paper successfully used Plassmann et al.’s (2015) framework to discuss five applications of neuroscience to marketing research. A review of growing EEG studies in the field of marketing and other interdisciplinary fields reveals the advantages and potential of using EEG in combination with other methods. This calls for more research using such methods.

A technical overview of ERP-related terminology provides researchers with a background for understanding and reviewing ERP studies. A discussion of method-related considerations and decisions provides marketing researchers with an introduction to the method and refers readers to relevant literature.

The marketing industry has been quick to adopt cutting edge technology, including EEG, to understand and predict consumer behavior for the purpose of improving marketing practices. This paper connects the academic and practitioner spheres by presenting past and potential EEG research that can be translatable to the marketing industry.

The authors review past literature on the use of EEG to study consumer-related topics in marketing and interdisciplinary fields, to demonstrate its advantages over-traditional methods in studying consumer-relevant behaviors. To foster increasing use of EEG in consumer neuroscience research, the authors further provide technical and marketing-specific considerations for both academic and market researchers. This paper is one of the first to review past EEG papers and provide methodological background insights for marketing researchers.

This study aims to investigate the effectiveness of two types of social media videos used for destination image development: induced/commercial-oriented content and organic content (where content is made without commercial interest, such as vlogs classified as user-generated content).

Experimental research using “Emotive EEG” (electroencephalogram) in a controlled environment was conducted with 30 participants (20 males, 10 females), age range 18 to 26. Emotive EEG recording was performed while the participants watched both types of video clips. Test results for both groups indicate that induced content is preferred over organic content.

This study opens up future research avenues where neuromarketing’s “Marketer Friendly” EEG equipment can be applied to the customer selection process.

Marketing analysts can gauge the interest and response of customers on different types of social media video content for destination marketing based on the findings of this study.

The study aims to evaluate the effect of inducing emotions (neutral, joy and fear) on the level of visual attention in beer advertisements.

A between-subject experimental study with a multi-method design was carried out using three neuroscience equipment concomitantly. The electroencephalogram and the electrical conductance sensor on the skin were used to assess the emotions induced in the individuals, while eye-tracking was used to assess the visual attention to beer advertisements. Three independent groups were formed. Each group was induced to one emotion (neutral, joy or fear), and then the level of visual attention was observed in ten stimuli of beer advertisements.

The results revealed that the induction of joy increased the visual attention to the brand name, while the induction of fear increased the visual attention to both the brand name and product packaging but reduced the visual attention to human faces within the ads.

This paper extends the literature, and to the best of the authors’ knowledge, it is the first study to indicate that induced emotions before ad viewing influence potential consumers’ visual attention.

The findings can serve as a basis for developing advertising campaigns that use emotion induction before ad viewing to increase the visual attention of potential consumers.

To the best of the authors’ knowledge, this is the first study to investigate whether the emotion induction that happens before ad viewing can impact the level of visual attention to advertisements. The study also provides clear and comprehensible implications from marketing practices to improve visual attention to ads.

This study aims to investigate the correlation between neural indexes of attention and behavioral indexes of attention and detect the most informative period of brain activity in which the strongest correlation with attentive performance (behavioral index) exists. Finally, to further validate the findings, this paper aims at the prediction of different levels of attention function based on the attention score obtained from repeatable battery for the assessment of neurophysiological status (RBANS).

The present paper analyzes electroencephalogram (EEG) signals recorded by a single prefrontal channel from 105 elderly subjects while they were responding to Stroop color test which is an attention-demanded task. Beside Stroop test, subjects also performed RBANS which provides their level of functionality in different domains including attention. After data acquisition (EEG during Stroop test and RBANS attention score), the authors extract the spectral features of EEG as neural indexes of attention and subjects’ reaction time in response to Stroop test as behavioral index of attention. Then, they explore the correlation between these post-cue frequency band oscillations of EEG with elderly response time (RT). Next, the authors exploit these findings to classify RBANS attention score.

The observations of this study suggest that there is significant negative correlation between alpha gamma ratio (AGR) and RT (p < 0.0001), theta beta ratio (TBR) is positively correlated with subjects’ RT (p < 0.0001), these correlations are stronger in a 500ms period right after triggering the cue (question onset in Stroop test), and 4) TBR and AGR can be effectively used to predict RBANS attention score.

Because of the experiment design, the pre-cue EEG of the next trail was very much overlapped with the post-cue EEG of the current trail. Therefore, the authors could analyze only post-cue EEG. In future study, it would be interesting to investigate the predictability of subject’s future performance from pre-cue EEG and mental preparation.

This study provides an insight into the research on detection of human attention level from EEG instead of conventional neurophysiological tests. It has also potential to be used in implementation of feasible and efficient EEG-based brain computer interface training systems for elderly.

To the best of the authors’ knowledge, this study is among very few attempts for early prediction of cognitive decline in the domain of attention from brain activity (EEG) instead of conventional tests which are prone to human errors.

The purpose of this study is to investigate the effects of the remote training process on distance learning with the application of neurometrics and investigate the features of the training that promote better synchronization between trainers and trainees in terms of cognitive and emotional processes favorable to learning, during a condition of remote professional training.

The authors proposed a hyperscanning paradigm together with a conversational analysis to assess remote online training by collecting neurophysiological measures (frequency band analysis: delta, theta, alpha and beta) via multiple wearable electroencephalograms (EEGs) during a session of remote training.

Results showed increased delta activity in the trainer when he was leading the session and when the participants were exchanging feedback. The delivery of feedback was also linked to increased theta activity compared with the normal activity of the trainees. Finally, synchronization of EEG between trainer and trainee groups was found for the beta band.

This study proposes to adopt a new multi-methodological approach that combines conversational analysis with the detection of remote neurometric parameters, in the field of educational neuroscience applied to organizational contexts.

Findings can help trainers in the development of their skills as trainers and in modeling remote training in organizations.

Findings highlight the crucial role of particular phases of the e-learning process, such as the feedback phase and the interaction trainer group, and they pointed out the relevance of neurophysiological measures to test the e-learning process.

This work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.

In this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.

For the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.

Epilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.